knitr::opts_chunk$set(echo = FALSE, message = FALSE)
library(Seurat)
library(ggplot2)
library(data.table)
library(MAST)
library(SingleR)
library(dplyr)
library(tidyr)
library(limma)
library(scRNAseq)
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.4
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] scRNAseq_2.2.0              limma_3.44.3               
##  [3] tidyr_1.1.1                 dplyr_1.0.2                
##  [5] SingleR_1.2.4               MAST_1.14.0                
##  [7] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.2
##  [9] DelayedArray_0.14.1         matrixStats_0.56.0         
## [11] Biobase_2.48.0              GenomicRanges_1.40.0       
## [13] GenomeInfoDb_1.24.2         IRanges_2.22.2             
## [15] S4Vectors_0.26.1            BiocGenerics_0.34.0        
## [17] data.table_1.13.0           ggplot2_3.3.2              
## [19] Seurat_3.2.0               
## 
## loaded via a namespace (and not attached):
##   [1] AnnotationHub_2.20.1          BiocFileCache_1.12.1         
##   [3] plyr_1.8.6                    igraph_1.2.5                 
##   [5] lazyeval_0.2.2                splines_4.0.2                
##   [7] BiocParallel_1.22.0           listenv_0.8.0                
##   [9] digest_0.6.25                 htmltools_0.5.0              
##  [11] magrittr_1.5                  memoise_1.1.0                
##  [13] tensor_1.5                    cluster_2.1.0                
##  [15] ROCR_1.0-11                   globals_0.12.5               
##  [17] colorspace_1.4-1              blob_1.2.1                   
##  [19] rappdirs_0.3.1                ggrepel_0.8.2                
##  [21] xfun_0.16                     crayon_1.3.4                 
##  [23] RCurl_1.98-1.2                jsonlite_1.7.0               
##  [25] spatstat_1.64-1               spatstat.data_1.4-3          
##  [27] survival_3.2-3                zoo_1.8-8                    
##  [29] ape_5.4-1                     glue_1.4.1                   
##  [31] polyclip_1.10-0               gtable_0.3.0                 
##  [33] zlibbioc_1.34.0               XVector_0.28.0               
##  [35] leiden_0.3.3                  BiocSingular_1.4.0           
##  [37] future.apply_1.6.0            abind_1.4-5                  
##  [39] scales_1.1.1                  DBI_1.1.0                    
##  [41] miniUI_0.1.1.1                Rcpp_1.0.5                   
##  [43] viridisLite_0.3.0             xtable_1.8-4                 
##  [45] reticulate_1.16               bit_4.0.4                    
##  [47] rsvd_1.0.3                    htmlwidgets_1.5.1            
##  [49] httr_1.4.2                    RColorBrewer_1.1-2           
##  [51] ellipsis_0.3.1                ica_1.0-2                    
##  [53] pkgconfig_2.0.3               uwot_0.1.8                   
##  [55] dbplyr_1.4.4                  deldir_0.1-28                
##  [57] tidyselect_1.1.0              rlang_0.4.7                  
##  [59] reshape2_1.4.4                later_1.1.0.1                
##  [61] AnnotationDbi_1.50.3          munsell_0.5.0                
##  [63] BiocVersion_3.11.1            tools_4.0.2                  
##  [65] generics_0.0.2                RSQLite_2.2.0                
##  [67] ExperimentHub_1.14.1          ggridges_0.5.2               
##  [69] evaluate_0.14                 stringr_1.4.0                
##  [71] fastmap_1.0.1                 yaml_2.2.1                   
##  [73] goftest_1.2-2                 knitr_1.29                   
##  [75] bit64_4.0.2                   fitdistrplus_1.1-1           
##  [77] purrr_0.3.4                   RANN_2.6.1                   
##  [79] pbapply_1.4-3                 future_1.18.0                
##  [81] nlme_3.1-148                  mime_0.9                     
##  [83] compiler_4.0.2                plotly_4.9.2.1               
##  [85] curl_4.3                      png_0.1-7                    
##  [87] interactiveDisplayBase_1.26.3 spatstat.utils_1.17-0        
##  [89] tibble_3.0.3                  stringi_1.4.6                
##  [91] lattice_0.20-41               Matrix_1.2-18                
##  [93] vctrs_0.3.2                   pillar_1.4.6                 
##  [95] lifecycle_0.2.0               BiocManager_1.30.10          
##  [97] lmtest_0.9-37                 RcppAnnoy_0.0.16             
##  [99] BiocNeighbors_1.6.0           cowplot_1.0.0                
## [101] bitops_1.0-6                  irlba_2.3.3                  
## [103] httpuv_1.5.4                  patchwork_1.0.1              
## [105] R6_2.4.1                      promises_1.1.1               
## [107] KernSmooth_2.23-17            gridExtra_2.3                
## [109] codetools_0.2-16              MASS_7.3-52                  
## [111] assertthat_0.2.1              withr_2.2.0                  
## [113] sctransform_0.2.1             GenomeInfoDbData_1.2.3       
## [115] mgcv_1.8-31                   grid_4.0.2                   
## [117] rpart_4.1-15                  rmarkdown_2.3                
## [119] DelayedMatrixStats_1.10.1     Rtsne_0.15                   
## [121] shiny_1.5.0

## Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
## Please use `as_label()` or `as_name()` instead.
## This warning is displayed once per session.

This File

Going to analyze the MEP cluster, which is of greatest interest to us.

Goal

To see if this MEP cluster is playing a role in fibrogenesis.

Distinguishing MEP Cluster

Looking to find what genes distinguish this MEP cluster from all other clusters in the analysis.

MEP Up-regulated markers

This was also done for down-regulated markers, but it is not very informative as there are few genes that are widely expressed in all other clusters excluding MEP cluster.

Subclustering

Going to look at a few different subclusterings:

  1. MEP + Megakaryocyte + Erythrocyte
  2. MEP + ?GMP + ?CMP
  3. MEP + Megakaryocyte + Erythrocyte + ?GMP + ?CMP
  4. MEP

Note: the original plan was to do this in Seurat, but this is where Monocle may be more useful to look at trajectories.

MEP + Megakaryocyte + Erythrocyte

## [1] "Cells in Subset 1"
## 
##        Gran-1        Gran-2          ?GMP      B cell-1        Gran-3 
##             0             0             0             0             0 
##      Monocyte     ?MEP/Mast   ?CMP/Neutro    Macrophage      B cell-2 
##             0           592             0             0             0 
##   Erythrocyte        T cell Megakaryocyte      B cell-3      B cell-4 
##           312             0           178             0             0
##                
##                 enrMigr1 enrMpl enrNbeal_cntrl Migr1 Mpl Nbeal_cntrl
##   Gran-1               0      0              0     0   0           0
##   Gran-2               0      0              0     0   0           0
##   ?GMP                 0      0              0     0   0           0
##   B cell-1             0      0              0     0   0           0
##   Gran-3               0      0              0     0   0           0
##   Monocyte             0      0              0     0   0           0
##   ?MEP/Mast           12    500             10    11  51           8
##   ?CMP/Neutro          0      0              0     0   0           0
##   Macrophage           0      0              0     0   0           0
##   B cell-2             0      0              0     0   0           0
##   Erythrocyte         65     26             29    76 110           6
##   T cell               0      0              0     0   0           0
##   Megakaryocyte        8     25            121     6   8          10
##   B cell-3             0      0              0     0   0           0
##   B cell-4             0      0              0     0   0           0
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session

MEP + ?GMP + ?CMP

MEP + Megakaryocyte + Erythrocyte + ?GMP + ?CMP

MEP

## [1] "Cells in Subset 4 (MEPs)"
## 
##        Gran-1        Gran-2          ?GMP      B cell-1        Gran-3 
##             0             0             0             0             0 
##      Monocyte     ?MEP/Mast   ?CMP/Neutro    Macrophage      B cell-2 
##             0           592             0             0             0 
##   Erythrocyte        T cell Megakaryocyte      B cell-3      B cell-4 
##             0             0             0             0             0
##                
##                 enrMigr1 enrMpl enrNbeal_cntrl Migr1 Mpl Nbeal_cntrl
##   Gran-1               0      0              0     0   0           0
##   Gran-2               0      0              0     0   0           0
##   ?GMP                 0      0              0     0   0           0
##   B cell-1             0      0              0     0   0           0
##   Gran-3               0      0              0     0   0           0
##   Monocyte             0      0              0     0   0           0
##   ?MEP/Mast           12    500             10    11  51           8
##   ?CMP/Neutro          0      0              0     0   0           0
##   Macrophage           0      0              0     0   0           0
##   B cell-2             0      0              0     0   0           0
##   Erythrocyte          0      0              0     0   0           0
##   T cell               0      0              0     0   0           0
##   Megakaryocyte        0      0              0     0   0           0
##   B cell-3             0      0              0     0   0           0
##   B cell-4             0      0              0     0   0           0

Differential Expression

MEPs

##    
##     enrMigr1 enrMpl enrNbeal_cntrl Migr1 Mpl Nbeal_cntrl
##   0        6    166              2     2   9           0
##   1        0    165              0     0  14           0
##   2        0     75              0     2   5           0
##   3        4     32              8     6  16           8
##   4        0     55              0     0   3           0
##   5        2      7              0     1   4           0

Nbeal vs Migr1

## 
## Control   Migr1     Mpl 
##      18      23     551
## [1] 9 5

Mpl vs Migr1

## [1] 113   5
##                p_val avg_logFC pct.1 pct.2    p_val_adj
## Slpi    2.923376e-23  2.864115 0.964 0.739 5.294527e-19
## Akr1c18 2.491825e-15  2.638171 0.835 0.043 4.512945e-11
## Ccl4    6.282744e-12  2.556199 0.911 0.391 1.137868e-07
## Furin   1.340648e-30  1.838023 0.989 0.783 2.428047e-26
## Cfp     5.628861e-16  1.594472 0.940 0.348 1.019443e-11
## Ccl6    4.286424e-21  1.450810 0.978 0.739 7.763142e-17

Mpl vs Migr1 & Nbeal Controls

## [1] 185   5
##                p_val avg_logFC pct.1 pct.2    p_val_adj
## Slpi    7.031209e-39  2.962248 0.964 0.707 1.273422e-34
## Ccl4    9.744238e-24  2.864216 0.911 0.317 1.764779e-19
## Akr1c18 2.707028e-27  2.753610 0.835 0.024 4.902698e-23
## Furin   4.255144e-54  2.032867 0.989 0.659 7.706491e-50
## Ccl6    3.703546e-53  1.983642 0.978 0.634 6.707492e-49
## S100a6  1.539080e-71  1.865602 0.998 0.561 2.787428e-67

GO Terms

Looking at the GO terms that are most associated with up- and down-regulated genes from Mpl compared to Nbeal and Migr1. These will be located in a supplementary excel file. See MEP.mpl.vs.migr1ANDnbeal.xlsx

MEP Subclusters